DESCRIPTION (from abstract): A major goal of research in Clinical Pharmacology is to characterize and understand the Therapeutic Response Surface, the quantitative relationships between patient factors, drug dosage, drug exposure, and drug effects. Research questions largely justify this goal, but more practical response surface information is required (i) to optimally individualize pharmacotherapeutics, and (ii) to efficiently carry out clinical drug development. Model-based (explanatory) analyses of PK/PD data arising from many sources, including non-classical studies, is increasingly seen as a means to gain knowledge about the response surface. The data analysis methods we have studied and plan to continue to study provide the inferential tools that are necessary for explanatory analysis of PK/PD data. During the requested continuation period we specifically propose to address the following areas: 1. Statistical Methods for Explanatory Analyses of Population PK/PD Data. We plan to further develop and study improved estimation methods, paying particular attention to their application to (i) heteroscedastic, and distributionally skewed data, (ii) polychotomous and failure-time PD data, (iii) multivariate response PD data, (iv) distributionally skewed and multimodal interindividual random effects, and (v) the effects of model misspecification. 2. Implementation in NONMEM. New methods developed under aim #1 will be implemented in NONMEM. Simpler means for the user-specification of models for polychotomous and other types of PD data will be implemented. Automatic means for computing profile likelihood based confidence intervals will be investigated and implemented. Certain types of kinetic models will be better accommodated. 3. Models and Methods for Compliance. We will investigate ways to use quantitative compliance data from electronic medication event monitors in explanatory population PK/PD analyses, with the additional goal of describing drug taking behavior itself.